Denoise PET Images Based on a Combining Method of EMD and ICA

摘要

The incidental component in addition to the measured target signals is considered as noise of Positron Emission Tomography (PET) images. A novel method to denoise the PET images based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) associated with Sparse Code Shrinkage (SCS) technique is proposed in this paper. EMD is executed to decompose a PET image into a number of Intrinsic Mode Functions (IMFs), which are used to reconstruct a new PET image after chosen by means of an inverse EMD procedure. By applying ICA to the new PET image, an orthogonal dataset can be obtained and the signal-noise separation can be realized. Then a clearer PET image can be reconstructed by SCS. The simulation results indicate that the proposed method is effective to denoise PET images.